Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

2010 ◽  
Vol 132 (7) ◽  
Author(s):  
Ling-Xiao Zhao ◽  
Liang Yang ◽  
Chun-Lu Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Cooling Capacity ◽  
Neural Network Modeling ◽  
Pressure Drops ◽  
Data Errors ◽  
Single Output ◽  
Dry And Wet Conditions ◽  
And Training

A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%.

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

Neural network modeling of the implosion process in the tasks of optimization of oil production process

2021 ◽  
Author(s):  
A.R. Mukhutdinov ◽  
Z.R. Vakhidova ◽  
M.G. Efimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Water Hammer ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Artificial Neural

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

Studying the innovative development of regional economy as an imperative of sustainable socio-economic growth in Russia, using neural network modeling

2021 ◽  
Vol 20 (8) ◽  
pp. 1394-1414
Author(s):  
Nikolai P. LYUBUSHIN ◽  
Elena N. LETYAGINA ◽  
Valentina I. PEROVA
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Neural Networks ◽  
Russian Federation ◽  
Network Modeling ◽  
Innovation Activity ◽  
Neural Network Modeling ◽  
Innovative Development ◽  
Russian Regions ◽  
The Russian Federation

Subject. The article deals with the innovative potential of Russian regions in light of the national goal of the Russian Federation development, reflecting decent and productive work. Objectives. The purpose is to study the innovation activity in Russian regions, using neural networks, to ensure breakthrough innovative development of the Russian economy. Methods. We employ a cluster analysis on the basis of neural network modeling, using information technologies. For the research, we selected neural networks (Kohonen self-organizing maps), which are focused on unsupervised learning and are a promising tool for clustering and visualization of multidimensional statistical data. Results. The result of neural network modeling was the ranking of 85 regions of the Russian Federation into 5 compact groups (clusters) regardless of their affiliation to federal districts of the Russian Federation. The study shows that there is a strong differentiation of the number of regions in these clusters. We obtained average values of indicators in the clusters and compared them with all-Russian indicators. Conclusions. Breakthrough in the socio-economic growth of the Russian Federation is associated with a set of measures that involve stimulating innovation activities in regions, which are characterized by different level of innovation development. Such measures will increase the interest of the real sector of the economy in using scientific development, advanced production technologies, higher-productivity employment opportunities, and, as a result, will encourage socio-economic growth and people's quality of life.

scholarly journals Skill Oriented Online Master’s Course “Neural Network Modeling of Complex Technical Systems”

2020 ◽  
Vol 35 ◽  
pp. 01011
Author(s):  
Ekaterina V. Panfilova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Research Work ◽  
Network Models ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Study Results ◽  
Complex Technical Systems ◽  
Technical Systems

In this paper we consider the application of online course “Neural Network modeling of Complex Technical Systems” in the Master’s degree programs in the field of nanotechnology and nanoengineering in Bauman Moscow State Technical University. The course has rather practical than theoretical nature. The aim of this course is skill oriented learning. Nowadays neural network models have become a powerful tool of scientific research for engineers and students. The methods studied during the study of the discipline can be applied to estimation, modeling, classification, clustering, forecasting and more. The neural networks modeling plays a significant role in Master’s education and student’s research work. Neural Networks models are successfully presented in graduation theses. Thanks to online educations students can practice at their own pace and study modern neural networks software products, methods of data preparing, designing and training neural network and then apply these algorithms in practice. According to the steps of neural network modeling algorithm the course consists of three main parts and conclusive one. In this paper course structure and study results are presented.

scholarly journals Direct and inverse neural network modeling in free radical polymerization

2004 ◽  
Vol 2 (1) ◽  
pp. 113-140 ◽  
Author(s):  
Silvia Curteanu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Free Radical ◽  
Radical Polymerization ◽  
Network Modeling ◽  
Free Radical Polymerization ◽  
Monomer Conversion ◽  
Polymerization Reaction ◽  
Neural Network Modeling ◽  
Hidden Neurons

AbstractThe first part of this paper reviews of the most important aspects regarding the use of neural networks in the polymerization reaction engineering. Then, direct and inverse neural network modeling of the batch, bulk free radical polymerization of methyl methacrylate is performed. To obtain monomer conversion, number and weight average molecular weights, and mass reaction viscosity, separate neural networks and, a network with multiple outputs were built (direct neural network modeling). The inverse neural network modeling gives the reaction conditions (temperature and initial initiator concentration) that assure certain values of conversion and polymerization degree at the end of the reaction. Each network is a multi-layer perceptron with one or two hidden layers and a different number of hidden neurons. The best topology correlates with the smallest error at the end of the training phase. The possibility of obtaining accurate results is demonstrated with a relatively simple architecture of the networks. Two types of neural network modeling, direct and inverse, represent possible alternatives to classical procedures of modeling and optimization, each producing accurate results and having simple methodologies.

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